EP 4200744 A1 20230628 - CORRECTING DIFFERENCES IN MULTI-SCANNERS FOR DIGITAL PATHOLOGY IMAGES USING DEEP LEARNING
Title (en)
CORRECTING DIFFERENCES IN MULTI-SCANNERS FOR DIGITAL PATHOLOGY IMAGES USING DEEP LEARNING
Title (de)
KORREKTUR VON UNTERSCHIEDEN IN MEHREREN SCANNERN FÜR DIGITALE KRANKHEITSBILDER UNTER VERWENDUNG VON TIEFENLERNEN
Title (fr)
CORRECTION DE DIFFÉRENCES DANS DES SCANNERS MULTIPLES POUR DES IMAGES DE PATHOLOGIE NUMÉRIQUES À L'AIDE D'UN APPRENTISSAGE PROFOND
Publication
Application
Priority
- US 202063068585 P 20200821
- US 2021046678 W 20210819
Abstract (en)
[origin: WO2022040418A1] The present disclosure relates to techniques for transforming digital pathology images obtained by different slide scanners into a common format for image analysis. Particularly, aspects of the present disclosure are directed to obtaining a source image of a biological specimen, the source image is generated from a first type of scanner, inputting into a generator model a randomly generated noise vector and a latent feature vector from the source image as input data, generating, by the generator model, a new image based on the input data, inputting into a discriminator model the new image, generating, by the discriminator model, a probability for the new image being authentic or fake, determining whether the new image is authentic or fake based on the generated probability, and outputting the new image when the image is authentic.
CPC (source: EP US)
G06F 18/2185 (2023.01 - EP); G06F 18/22 (2023.01 - EP); G06T 7/0012 (2013.01 - US); G06V 20/698 (2022.01 - EP); G06T 2207/20084 (2013.01 - US)
Designated contracting state (EPC)
AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR
Designated extension state (EPC)
BA ME
Designated validation state (EPC)
KH MA MD TN
DOCDB simple family (publication)
WO 2022040418 A1 20220224; CN 115917612 A 20230404; EP 4200744 A1 20230628; JP 2023538104 A 20230906; JP 7505116 B2 20240624; US 2023230242 A1 20230720
DOCDB simple family (application)
US 2021046678 W 20210819; CN 202180050720 A 20210819; EP 21769586 A 20210819; JP 2023512309 A 20210819; US 202318170788 A 20230217